Guanyu Chena,b, Zhihua Caia,b, and Xiang Lia,b

(Submitted on December 12, 2018; Revised on January 15, 2019; Accepted on February 10, 2019)

Abstract:

With the network model architecture of Google Inception, research is conducted on issues such as the structural design of the model, data preprocessing, tuning of training parameters, computing clusters in a distributed environment, and multi-machine parallel training. According to the performances of different deep neural network models on different data sets, the Google Inception V3 depth network model is used as the prototype to conduct the tuning of training parameters, and the classification of remote sensing images is then realized with this model in the single-machine environment. Furthermore, due to the effectiveness of distributed systems for very large data sets and compute-intensive applications, a data parallel training scheme based on the distributed platform is designed for the convolution neural network model with more complex data form, larger quantity of parameters, and more network levels, after studying the mainstream designs of the distributed machine learning and analyzing the training methods and steps of the convolutional neural network model in a multi-machine environment. It greatly improves the training time of the model, and then the classification of remote sensing images under distributed clusters is realized.